Add resampling for fused location

replace/6c25d3b7ae46316fe9563c4e1709e6d35d3b3f77
JulioV 2019-12-09 18:23:00 -05:00
parent 6a79fbe1e8
commit 0ba88203f4
3 changed files with 63 additions and 1 deletions

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@ -46,6 +46,11 @@ PHONE_VALID_SENSED_DAYS:
MIN_VALID_HOURS: 20 # (out of 24)
MIN_BINS_PER_HOUR: 8 # (out of 60min/BIN_SIZE bins)
RESAMPLE_FUSED_LOCATION:
CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold
TIME_SINCE_VALID_LOCATION: 12 # hours, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row
TIMEZONE: *timezone
BARNETT_LOCATION:
ACCURACY_LIMIT: 51 # filters location coordinates with an accuracy higher than this
TIMEZONE: *timezone

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@ -52,3 +52,17 @@ rule unify_ios_android:
"data/raw/{pid}/{sensor}_with_datetime_unified.csv"
script:
"../src/data/unify_ios_android.R"
rule resample_fused_location:
input:
locations = "data/raw/{pid}/locations_raw.csv",
phone_sensed_bins = rules.phone_sensed_bins.output
params:
bin_size = config["PHONE_VALID_SENSED_DAYS"]["BIN_SIZE"],
timezone = config["RESAMPLE_FUSED_LOCATION"]["TIMEZONE"],
consecutive_threshold = config["RESAMPLE_FUSED_LOCATION"]["CONSECUTIVE_THRESHOLD"],
time_since_valid_location = config["RESAMPLE_FUSED_LOCATION"]["TIME_SINCE_VALID_LOCATION"]
output:
"data/raw/{pid}/locations_resampled.csv"
script:
"../src/data/resample_fused_location.R"

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@ -0,0 +1,43 @@
source("packrat/init.R")
library(dplyr)
library(readr)
library(tidyr)
bin_size <- snakemake@params[["bin_size"]]
timezone <- snakemake@params[["timezone"]]
consecutive_threshold <- snakemake@params[["consecutive_threshold"]]
time_since_valid_location <- snakemake@params[["time_since_valid_location"]]
locations <- read_csv(snakemake@input[["locations"]], col_types = cols())
phone_sensed_bins <- read_csv(snakemake@input[["phone_sensed_bins"]], col_types = cols(local_date = col_character()))
if(nrow(locations) > 0){
sensed_minute_bins <- phone_sensed_bins %>%
pivot_longer(-local_date, names_to = c("hour", "bin"), names_ptypes = list(hour = integer(), bin = integer()), names_sep = "_", values_to = "sensor_count") %>%
complete(nesting(local_date, hour), bin = seq(0, 59,1)) %>%
fill(sensor_count) %>%
mutate(timestamp = as.numeric(as.POSIXct(paste0(local_date, " ", hour,":", bin,":00"), format = "%Y-%m-%d %H:%M:%S", tz = timezone)) * 1000 ) %>%
filter(sensor_count > 0) %>%
select(timestamp)
resampled_locations <- locations %>%
filter(provider == "fused") %>%
bind_rows(sensed_minute_bins) %>%
arrange(timestamp) %>%
# We group and therefore, fill in, missing rows that appear after a valid fused location record and exist
# within consecutive_threshold minutes from each other
mutate(consecutive_time_diff = c(1, diff(timestamp)),
resample_group = cumsum(!is.na(double_longitude) | consecutive_time_diff > (1000 * 60 * consecutive_threshold))) %>%
group_by(resample_group) %>%
# drop rows that are logged after time_since_valid_location hours from the last valid fused location
filter((timestamp - first(timestamp) < (1000 * 60 * 60 * time_since_valid_location))) %>%
fill(-timestamp, -resample_group) %>%
select(-consecutive_time_diff) %>%
drop_na(double_longitude, double_latitude, accuracy)
write.csv(resampled_locations,snakemake@output[[1]], row.names = F)
} else {
write.csv(locations,snakemake@output[[1]], row.names = F)
}